AI Coding Live News

Tackling long-horizon problem solving and scientific discovery requires computation to compound across successive attempts. While humans build upon partial proofs, failed experiments, and prior hypotheses, current test-time compute paradigms (e.g., MCTS and Best-of-N) isolate search trajectories into independent silos, discarding intermediate reasoning artifacts upon branch failure. Researchers introduce compounding inference and its open-source instantiation GitSwarm (arXiv:2610.04862). GitSwarm establishes an asynchronous multi-agent coordination architecture where homogeneous LLM agents collaborate through structured persistent memory hosted in a shared, branchable Git repository. Intermediate artifacts are preserved as atomic commits, while explicit semantic dependencies link cross-branch contributions, enabling agents to inspect, branch, verify, and synthesize past discoveries. Evaluated on rigorous long-horizon benchmarks, GitSwarm paired with GPT-5.5 solves all 30 problems on IMOProofBench-Advanced in a single execution run, and scores 79.4% on ProgramBench compared to 65.1% for the strongest reported baseline under identical budgets.

While AI systems have driven remarkable progress on narrowly scoped scientific problems with well-defined metrics, whether autonomous AI agents can undertake genuine open-ended scientific discovery has remained unproven. Researchers present Station (arXiv:2610.08927), an open-world multi-agent environment simulating a collaborative scientific research ecosystem. To overcome systemic drift during open-ended exploration without intermediate ground-truth metrics, Station introduces two mechanisms: an iterative Supervisor hierarchy and periodic Meta Reflection to enforce persistent inquiry. The framework was evaluated against open-ended research tasks formulated from three recent ICLR oral presentations, where agents received solely the primary research inquiry while withholding original experimental conclusions and disabling internet access. Station autonomously rediscovers an average of 62.7% of the original scientific criteria, compared to just 15.4% for Codex Multiagent-v2 and 14.4% to 20.6% for AI Scientist-v2. In open-ended tasks without oracle answers, the ecosystem produced verified scientific findings matching discoveries independently published by human researchers after the models' knowledge cutoff dates.

GPT-6 Astra is now live for all Pro, Enterprise, and Business Premium users in ChatGPT Work and Codex, and in the API. OpenAI calls it the world's most intelligent and aligned model, with new state-of-the-art results on computer use, software engineering, and scientific discovery.

OpenAI says GPT-6 Astra is state-of-the-art on FrontierMath Tier 4, ARC-AGI 3, and TerminalBench-4.0, and also leads on Terminal-Bench Science 0.1 and HealthBench Pro—highlighting agentic terminal and scientific discovery strength.